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Record W2915635372 · doi:10.1080/09513590.2019.1580259

What is the optimal timing of embryo transfer when there are only one or two embryos at cleavage stage?

2019· article· en· W2915635372 on OpenAlexaff
Jigal Haas, Jim Meriano, Rawad Bassil, Eran Barzilay, Robert F. Casper

Bibliographic record

VenueGynecological Endocrinology · 2019
Typearticle
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsTranslational Research in OncologyUniversity of Toronto
Fundersnot available
KeywordsEmbryoEmbryo transferCleavage (geology)AndrologyStage (stratigraphy)BiologyCell biologyGynecologyChemistryMedicine

Abstract

fetched live from OpenAlex

Today, most IVF programs have moved to blastocyst transfer but there is still uncertainty regarding when to transfer if there are only one or two embryos at the cleavage stage. The aim of this study was to compare the pregnancy rate of day 3 transfers vs. blastocyst stage transfers in patients who had only one or two embryos on day 3. We conducted a retrospective study of 102 patients with one or two cleavage stage embryos that had their embryos transferred on day 3 and 429 patients had their embryos cultured to day 5 for transfer. The number of mature oocytes (4.0 vs 4.6, p = NS) and number of cleavage stage embryos on day 3 was similar in the two groups (1.3 vs. 1.5, p = NS). The clinical pregnancy rate per retrieval (22% vs. 24.6%, p= NS) and the ongoing pregnancy rate per retrieval (20% vs. 20.2%, p = NS) was comparable between the groups. Fifty seven (13.2%) of the patients had cleavage embryo arrest and did not have an embryo to transfer on day 5. We conclude that the cumulative pregnancy rate is the same for patients with 1–2 cleavage stage embryos regardless of whether the embryo is transferred on day 3 or day 5.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.295
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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